For many applications of wearable computing devices, the ability to detect aspects of a user's internal or external state is essential. Generally, this is called context awareness.
Internal context aware computing has been used to detect a variety of aspects of a user's internal state. For example, sensors have measured psychological patterns of the user, such as expressions of joy or anger, and these psychological parameters have been correlated with physiological parameters such as heart rate and physical activity. Actigraphy, i.e., processed accelerometer readings, has been used for detailed and long-term measurement of hand tremors to help Parkinson patients manage their symptoms. Wearable computing devices have also been used for detecting when a person is sleeping, and for correlating between levels of activity and mood disorders, and attention-deficit hyperactivity disorder.
External context aware computing has been used in applications such as automated tour guides, and for people and animal tracking,. With automated tour guides, the wearable devices augment the user's view of attractions.
Wearable devices can also provide navigational assistance for visually impaired people. GPS information acquired by wearable devices has been used in context-aware computing to assist field workers track wild animals.
For the purpose of navigating, most prior art work has focused on rich and well understood sensors such as optical, vision, and audio sensors. For example, U.S. Pat. No. 5,793,483 describes an optical measurement system that includes an optical beam transmitter, and an optical beam detector to measure a distance between the transmitter and detector. U.S. Pat. No. 5,378,969 describes a system for controlling robot navigation that uses a map of the environment that was prepared in advance. In the map, shape features of objects are predesignated. The robot has a vision sensor. In its navigation, the robot recognizes its current position from the shape feature.
In the field of robot navigation, many systems also rely on sonar sensors, which are not appropriate for wearable systems where control of orientation is a problem.
For indoor navigation, GPS is difficult to use. U.S. Pat. No. 5,959,575, describes an interior GPS navigation method. There, ground transceivers transmit a pseudo-satellite signal which includes positional coordinates of the round transceiver. Active badges and systems based on beacons require the installation and maintenance of complex and expensive infrastructures.
It is desired to perform indoor navigation using only passive wearable sensors that rely entirely on naturally-occurring, signals. A challenge with this type of application, and other related ones, is that the raw sensor signals are often unsuitable for use as direct inputs to a machine-learning process to model an environment. The reason is that there is too great a statistical distance between the raw signals and the high-level inference that is to be made. One goal of the invention is to increase the likelihood that a correct inference is made.